Architecture-Aware Minimization (A$^2$M): How to Find Flat Minima in Neural Architecture Search

• URL: http://arxiv.org/abs/2503.10404v1
• Date: Thu, 13 Mar 2025 14:30:17 GMT
• Title: Architecture-Aware Minimization (A$^2$M): How to Find Flat Minima in Neural Architecture Search
• Authors: Matteo Gambella, Fabrizio Pittorino, Manuel Roveri,
• Abstract summary: We investigate the geometric properties of neural architecture spaces commonly used in differentiable NAS methods.<n>Building on these insights, we propose Architecture-Aware Minimization (A$2$M), a novel analytically derived algorithmic framework.<n>A$2$M consistently improves generalization over state-of-the-art DARTS-based algorithms on benchmark datasets.
• Score: 3.724847012963521
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural Architecture Search (NAS) has become an essential tool for designing effective and efficient neural networks. In this paper, we investigate the geometric properties of neural architecture spaces commonly used in differentiable NAS methods, specifically NAS-Bench-201 and DARTS. By defining flatness metrics such as neighborhoods and loss barriers along paths in architecture space, we reveal locality and flatness characteristics analogous to the well-known properties of neural network loss landscapes in weight space. In particular, we find that highly accurate architectures cluster together in flat regions, while suboptimal architectures remain isolated, unveiling the detailed geometrical structure of the architecture search landscape. Building on these insights, we propose Architecture-Aware Minimization (A$^2$M), a novel analytically derived algorithmic framework that explicitly biases, for the first time, the gradient of differentiable NAS methods towards flat minima in architecture space. A$^2$M consistently improves generalization over state-of-the-art DARTS-based algorithms on benchmark datasets including CIFAR-10, CIFAR-100, and ImageNet16-120, across both NAS-Bench-201 and DARTS search spaces. Notably, A$^2$M is able to increase the test accuracy, on average across different differentiable NAS methods, by +3.60\% on CIFAR-10, +4.60\% on CIFAR-100, and +3.64\% on ImageNet16-120, demonstrating its superior effectiveness in practice. A$^2$M can be easily integrated into existing differentiable NAS frameworks, offering a versatile tool for future research and applications in automated machine learning. We open-source our code at https://github.com/AI-Tech-Research-Lab/AsquaredM.

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